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---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base_ai4privacy_en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base_ai4privacy_en
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0962
- Overall Precision: 0.8739
- Overall Recall: 0.9046
- Overall F1: 0.8890
- Overall Accuracy: 0.9623
- Accountname F1: 0.9898
- Accountnumber F1: 0.9896
- Age F1: 0.8745
- Amount F1: 0.8663
- Bic F1: 0.8782
- Bitcoinaddress F1: 0.9414
- Buildingnumber F1: 0.8279
- City F1: 0.8312
- Companyname F1: 0.9434
- County F1: 0.9279
- Creditcardcvv F1: 0.8947
- Creditcardissuer F1: 0.9755
- Creditcardnumber F1: 0.8770
- Currency F1: 0.6753
- Currencycode F1: 0.6398
- Currencyname F1: 0.2105
- Currencysymbol F1: 0.9223
- Date F1: 0.8276
- Dob F1: 0.5470
- Email F1: 0.9840
- Ethereumaddress F1: 0.9972
- Eyecolor F1: 0.9027
- Firstname F1: 0.8696
- Gender F1: 0.9627
- Height F1: 0.9811
- Iban F1: 0.9912
- Ip F1: 0.0124
- Ipv4 F1: 0.8377
- Ipv6 F1: 0.7585
- Jobarea F1: 0.8212
- Jobtitle F1: 0.9833
- Jobtype F1: 0.9110
- Lastname F1: 0.8305
- Litecoinaddress F1: 0.8793
- Mac F1: 0.9957
- Maskednumber F1: 0.8315
- Middlename F1: 0.9441
- Nearbygpscoordinate F1: 0.9970
- Ordinaldirection F1: 0.9682
- Password F1: 0.9654
- Phoneimei F1: 0.9944
- Phonenumber F1: 0.9860
- Pin F1: 0.8150
- Prefix F1: 0.9306
- Secondaryaddress F1: 0.9935
- Sex F1: 0.9721
- Ssn F1: 0.9759
- State F1: 0.8817
- Street F1: 0.8264
- Time F1: 0.9485
- Url F1: 0.9936
- Useragent F1: 0.9976
- Username F1: 0.9108
- Vehiclevin F1: 0.9568
- Vehiclevrm F1: 0.9239
- Zipcode F1: 0.8543
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Accountname F1 | Accountnumber F1 | Age F1 | Amount F1 | Bic F1 | Bitcoinaddress F1 | Buildingnumber F1 | City F1 | Companyname F1 | County F1 | Creditcardcvv F1 | Creditcardissuer F1 | Creditcardnumber F1 | Currency F1 | Currencycode F1 | Currencyname F1 | Currencysymbol F1 | Date F1 | Dob F1 | Email F1 | Ethereumaddress F1 | Eyecolor F1 | Firstname F1 | Gender F1 | Height F1 | Iban F1 | Ip F1 | Ipv4 F1 | Ipv6 F1 | Jobarea F1 | Jobtitle F1 | Jobtype F1 | Lastname F1 | Litecoinaddress F1 | Mac F1 | Maskednumber F1 | Middlename F1 | Nearbygpscoordinate F1 | Ordinaldirection F1 | Password F1 | Phoneimei F1 | Phonenumber F1 | Pin F1 | Prefix F1 | Secondaryaddress F1 | Sex F1 | Ssn F1 | State F1 | Street F1 | Time F1 | Url F1 | Useragent F1 | Username F1 | Vehiclevin F1 | Vehiclevrm F1 | Zipcode F1 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:|
| 0.3911 | 1.0 | 2175 | 0.2642 | 0.6000 | 0.6420 | 0.6203 | 0.9119 | 0.9177 | 0.8043 | 0.6417 | 0.2664 | 0.4444 | 0.7762 | 0.2639 | 0.3614 | 0.6320 | 0.5282 | 0.5097 | 0.8493 | 0.4381 | 0.2180 | 0.0 | 0.0 | 0.4754 | 0.6817 | 0.0 | 0.9518 | 0.9710 | 0.5435 | 0.5869 | 0.6865 | 0.5455 | 0.7382 | 0.0 | 0.7780 | 0.7260 | 0.3064 | 0.6099 | 0.5096 | 0.4491 | 0.5872 | 0.8521 | 0.4547 | 0.0365 | 0.9822 | 0.7915 | 0.7728 | 0.9553 | 0.8337 | 0.1159 | 0.8853 | 0.8930 | 0.9520 | 0.7916 | 0.1731 | 0.2881 | 0.7877 | 0.9793 | 0.9202 | 0.6108 | 0.7374 | 0.4551 | 0.5222 |
| 0.1748 | 2.0 | 4350 | 0.1410 | 0.7732 | 0.8035 | 0.7880 | 0.9479 | 0.9831 | 0.9575 | 0.8131 | 0.6771 | 0.8161 | 0.8795 | 0.6822 | 0.6022 | 0.8531 | 0.6954 | 0.8056 | 0.9663 | 0.8012 | 0.5330 | 0.3009 | 0.0571 | 0.8293 | 0.7760 | 0.3798 | 0.9646 | 0.9675 | 0.8677 | 0.6901 | 0.9239 | 0.9655 | 0.9073 | 0.0 | 0.8345 | 0.7913 | 0.7190 | 0.9331 | 0.8958 | 0.5220 | 0.7748 | 0.9913 | 0.7372 | 0.4010 | 0.9925 | 0.9558 | 0.8982 | 0.9359 | 0.9586 | 0.6094 | 0.8621 | 0.9891 | 0.9702 | 0.9464 | 0.5906 | 0.5943 | 0.9437 | 0.9936 | 0.9369 | 0.8300 | 0.9157 | 0.8207 | 0.7405 |
| 0.1081 | 3.0 | 6525 | 0.1143 | 0.8376 | 0.8825 | 0.8595 | 0.9559 | 0.9865 | 0.9803 | 0.8586 | 0.7828 | 0.8154 | 0.8297 | 0.7920 | 0.7957 | 0.9143 | 0.8413 | 0.8218 | 0.9628 | 0.8634 | 0.6290 | 0.5636 | 0.1324 | 0.8788 | 0.8283 | 0.4895 | 0.9797 | 0.9917 | 0.8895 | 0.8303 | 0.9294 | 0.9718 | 0.9746 | 0.0 | 0.8325 | 0.7976 | 0.7521 | 0.9647 | 0.9140 | 0.7495 | 0.7371 | 0.9848 | 0.7944 | 0.8836 | 0.9955 | 0.9701 | 0.9227 | 0.9944 | 0.9785 | 0.7427 | 0.9254 | 0.9924 | 0.9701 | 0.9527 | 0.8031 | 0.7519 | 0.9288 | 0.9929 | 0.9848 | 0.8880 | 0.9391 | 0.9251 | 0.8403 |
| 0.0804 | 4.0 | 8700 | 0.0962 | 0.8739 | 0.9046 | 0.8890 | 0.9623 | 0.9898 | 0.9896 | 0.8745 | 0.8663 | 0.8782 | 0.9414 | 0.8279 | 0.8312 | 0.9434 | 0.9279 | 0.8947 | 0.9755 | 0.8770 | 0.6753 | 0.6398 | 0.2105 | 0.9223 | 0.8276 | 0.5470 | 0.9840 | 0.9972 | 0.9027 | 0.8696 | 0.9627 | 0.9811 | 0.9912 | 0.0124 | 0.8377 | 0.7585 | 0.8212 | 0.9833 | 0.9110 | 0.8305 | 0.8793 | 0.9957 | 0.8315 | 0.9441 | 0.9970 | 0.9682 | 0.9654 | 0.9944 | 0.9860 | 0.8150 | 0.9306 | 0.9935 | 0.9721 | 0.9759 | 0.8817 | 0.8264 | 0.9485 | 0.9936 | 0.9976 | 0.9108 | 0.9568 | 0.9239 | 0.8543 |
| 0.0663 | 5.0 | 10875 | 0.0965 | 0.8761 | 0.9089 | 0.8922 | 0.9632 | 0.9882 | 0.9896 | 0.8845 | 0.8676 | 0.8750 | 0.9463 | 0.8280 | 0.8415 | 0.9482 | 0.9365 | 0.8954 | 0.9774 | 0.8897 | 0.6571 | 0.6773 | 0.2690 | 0.9217 | 0.8259 | 0.6135 | 0.9859 | 0.9972 | 0.9180 | 0.8840 | 0.9708 | 0.975 | 0.9667 | 0.1201 | 0.8109 | 0.7064 | 0.8298 | 0.9885 | 0.9265 | 0.8520 | 0.8844 | 0.9935 | 0.8523 | 0.9462 | 0.9985 | 0.9744 | 0.9682 | 0.9958 | 0.9881 | 0.8166 | 0.9333 | 0.9935 | 0.9721 | 0.9772 | 0.8889 | 0.8294 | 0.9632 | 0.9952 | 0.9976 | 0.9172 | 0.9538 | 0.9418 | 0.8638 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0.post200
- Datasets 2.10.1
- Tokenizers 0.13.3
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